Wearing a heart rate monitor during exercise is great, but wearing it after the exercise is important too! HRR-60
This report analyzes heart rate variability and recovery during a treadmill session featuring intervals transitioning between 12 min/mile and 8 min/mile. By applying Bayesian B-Splines, I decompose the signal into continuous acceleration metrics and extract, as metrics of fitness:
Punch: Max slope between two points.
HRR-60: Difference at \(T_{720}\) and \(T_{780}\).
Intervals,When HR > 150 bpm.
These can be compared across training sessions and build trends of improvement as I keep exercising.
/var/folders/kq/2zfcdcr577b47rsq8qxqmwjw0000gp/T/ipykernel_97982/991029321.py:146: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions
return cls(np.median(samples, axis=0), az.hdi(samples, hdi_prob=hdi_prob))
/var/folders/kq/2zfcdcr577b47rsq8qxqmwjw0000gp/T/ipykernel_97982/991029321.py:146: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions
return cls(np.median(samples, axis=0), az.hdi(samples, hdi_prob=hdi_prob))
/var/folders/kq/2zfcdcr577b47rsq8qxqmwjw0000gp/T/ipykernel_97982/991029321.py:146: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions
return cls(np.median(samples, axis=0), az.hdi(samples, hdi_prob=hdi_prob))
/var/folders/kq/2zfcdcr577b47rsq8qxqmwjw0000gp/T/ipykernel_97982/991029321.py:146: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions
return cls(np.median(samples, axis=0), az.hdi(samples, hdi_prob=hdi_prob))